Blockchain-Integrated AI Framework for Secure Data Sharing

The rapid expansion of digital ecosystems has made secure, transparent, and in-telligent data sharing a critical requirement for modern organizations. Traditional centralized systems continue to struggle with issues such as single points of fail-ure, weak auditability, and limited trust—especially when multiple institutions must collaborate on sensitive data. To address these limitations, this research pro-poses a Blockchain-Integrated AI Framework that combines decentralized trust mechanisms with privacy-preserving analytics. The framework leverages Ethere-um smart contracts to enforce automated access control and immutable record-keeping, ensuring that every data-sharing action remains verifiable and tamper-proof. Encrypted datasets are stored using a hybrid model comprising IPFS for decentralized file storage and MongoDB for metadata management, enabling both high availability and strong data integrity. A dedicated AI/ML microservice, de-veloped using Python, performs analytical tasks such as pattern detection, anoma-ly identification, and predictive modeling without exposing raw data to unauthor-ized entities. Access to this computation layer is entirely governed by smart-contract-based permissions, ensuring that only validated users or services can re-trieve encrypted data for analysis. The system’s architecture is supported by a Node.js backend and a React.js frontend, creating an end-to-end environment for secure data upload, permission management, and blockchain-based audit visuali-zation. Beyond secure storage, the framework emphasizes real-time intelligence, providing organizations with actionable insights while preserving strict confiden-tiality.